The present invention relates to a biometric authentication system using biometric information to verify identities.
Biometric authentication is known as an authentication technology with advantages that the forgery of information input for authentication (for example, fingerprint) is more difficult than the authentication based on the password, IC card, or other identifying information, and that the information input for authentication is not forgotten.
In the biometric authentication, biometric data is first acquired from a user, and information called the feature is extracted from the biometric data and is registered in advance. This registered information is called the enrolled template. The user who has been registered in advance is called the enrolled user. Upon authentication of a user, the biometric data is acquired from the user, and the feature extracted from the biometric data is checked against the enrolled template to perform authentication (identity verification). The user to be identified is called the claimant.
One of biometric authentication technologies that identifies a claimant to find out the identical enrolled user by matching the claimant with each of N enrolled users (hereinafter referred to as “1:N matching”), is called biometric identification. In conventional biometric identification, when an enrolled user identified as the claimant (hereinafter referred to as “identified user”) exists, authentication success is determined with the identified user as the determination result. While authentication failure is determined when no identified user exists. Examples of the biometric authentication system using biometric identification are a time and attendance management system, and a system for credit payment only using biometric authentication instead of using a credit card (hereinafter referred to as “cardless credit payment system”). Biometric identification does not require the claimant to present a card or other means of identification, and has an advantage of high availability.
FIG. 5 shows types of authentication error rate in biometric identification. The authentication error rate in biometric identification can be classified into the following three types.
(1) Error rate that occurs when a claimant previously registered is successfully authenticated as another enrolled user (hereinafter referred to as “Enrollee False Acceptance Rate: EFAR).
(2) Error rate that occurs when a claimant previously registered fails to be authenticated (hereinafter referred to as “Enrollee False Rejection Rate: EFRR”).
(3) Error rate that occurs when a user not previously registered (hereinafter referred to as “non-enrolled user”) is successfully authenticated (hereinafter referred to as “Non-Enrollee False Acceptance Rate: NFAR”).
When EFAR or NFAR is high the possibility that the claimant will be successfully authenticated as another person increases, resulting in a decrease in security. Such an authentication error of when the claimant is successfully authenticated as another person, is called erroneous identification. When EFRR is high the possibility that the registered claimant will fail to be authenticated increases, resulting in a decrease in availability.
There are authentication methods proposed to increase the authentication accuracy in biometric identification. This authentication method asks the claimant to input plural biometric information (for example, biometric information of different types such as fingerprint, iris, voice, or biometric information of the same type collected from different areas of the body such as index fingerprint, middle fingerprint, and fourth fingerprint), and integrates the input biometric data to determine the identity of the claimant (hereinafter referred to as “1:N combined authentication”). For example, in U.S. Pat. No. 7,277,891 B2, the claimant candidates (enrolled users who may possibly be identical to a claimant) are narrowed down by first biometric data (for example, face), and the remaining candidates are further narrowed down by second biometric data (for example, fingerprint). Finally the last remaining candidates are output as the determination result.
However, in 1:N combined authentication, the claimant needs to input plural types of biometric information, so that the authentication procedure is complicated, and availability decreases. In addition, the time from the input of the first biometric data to the output of the authentication result (hereinafter referred to as “authentication time”) is longer than the case of authentication using only one piece of biometric data, resulting in a further decrease in availability. However, no measure has been taken to solve such problems in U.S. Pat. No. 7,277,891 B2.
In U.S. Pat. No. 7,277,891 B2, the system may output plural candidates. For example, in an application such as cardless credit payment, it is necessary to narrow down possible claimants to only one user. Thus, one identified user is manually selected after plural candidates are output, resulting in a further decrease in availability.
An approach to solve the problem is proposed in Hideki Noda, “Sequential Probability Ratio Test for Adaptive Speaker Identification”, IEICE technical report D-II Vol. J84-D-II, No. 1, pp. 211-213 (2001). This approach uses the distribution pn (xj) of the features of enrolled users un (n=1 to N) and the distribution p0(xJ) of the feature of all enrolled users, to calculate the likelihood ratio ln for each of the enrolled users, each time the voice feature xJ (J=1, 2, and so on) is acquired, by the following equation.
                              I          n                =                              ∏                          j              =              1                        J                    ⁢                                          ⁢                                                    P                n                            ⁡                              (                                  x                  j                                )                                      /                                          P                0                            ⁡                              (                                  x                  j                                )                                                                        (        1        )            
When the obtained likelihood ratio ln is larger than a threshold A, authentication success is determined with the enrolled user un at this time as the identified user. When the obtained likelihood ratio ln is smaller than a threshold B, one or more enrolled users un corresponding to the likelihood ratio ln are excluded from the matching target in the subsequent steps, which is hereinafter referred to as “pruning”. When no identified user is obtained, another feature xJ is acquired to repeat determination until the identified user is obtained. As described above, the determination of the claimant is performed by comparing the likelihood ratio with the threshold A each time the feature is acquired, in order to reduce the number of inputs of the biometric data necessary for authentication. Further, the time for 1:N matching is substantially proportional to the number of enrolled users N to be matched. Thus, the time for 1:N matching is reduced by pruning the enrolled user(s) based on the result of comparing each of the likelihood ratios with the threshold B. In this way, the authentication time is further reduced.